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Enhanced chained and Cuckoo hashing methods for multi-core CPUs
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dc.contributor.author Kim, Euihyeok -
dc.contributor.author Kim, Min-Soo -
dc.date.available 2017-07-11T06:19:12Z -
dc.date.created 2017-04-10 -
dc.date.issued 2014-09 -
dc.identifier.issn 1386-7857 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/3051 -
dc.description.abstract A hash table is a fundamental data structure implementing an associative memory that maps a key to its associative value. Besides, the paradigm of micro-architecture design of CPUs is shifting away from faster uniprocessors toward slower chip multiprocessors. In this paper, we propose enhanced chained hashing and Cuckoo hashing methods for modern computers having a lot of CPU cores with exploiting CPU cache line and hardware level lock-free operations. The proposed methods outperform the existing methods in most cases and are very scalable in terms of the number of CPU cores. In addition, their performances do not degrade much even with a high fill factor (e.g., 90 %). Through extensive experiments using Intel 32-core machine, we have shown our proposed methods improve performance compared with the state-of-the-art version of the four exiting major hashing methods of linear, chained, Cuckoo, and Hopscotch. © 2014 Springer Science+Business Media New York. -
dc.publisher Springer -
dc.title Enhanced chained and Cuckoo hashing methods for multi-core CPUs -
dc.type Article -
dc.identifier.doi 10.1007/s10586-013-0343-y -
dc.identifier.scopusid 2-s2.0-84906781343 -
dc.identifier.bibliographicCitation Kim, Euihyeok. (2014-09). Enhanced chained and Cuckoo hashing methods for multi-core CPUs. Cluster Computing: the Journal of Networks Software Tools and Applications, 17(3), 665–680. doi: 10.1007/s10586-013-0343-y -
dc.subject.keywordAuthor Linear hashing -
dc.subject.keywordAuthor Chained hashing -
dc.subject.keywordAuthor Cuckoo hashing -
dc.subject.keywordAuthor Hopscotch hashing -
dc.subject.keywordAuthor Lock-free -
dc.subject.keywordAuthor Cache line -
dc.citation.endPage 680 -
dc.citation.number 3 -
dc.citation.startPage 665 -
dc.citation.title Cluster Computing: the Journal of Networks Software Tools and Applications -
dc.citation.volume 17 -
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